


import tensorflow as tf

from ...tf_utils import shape_list







class 							lowerCamelCase__						(				tf.keras.layers.Layer   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case=1   ,    snake_case=False   ,    **snake_case  ):

						'''simple docstring'''


						super().__init__(**snake_case  )

						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							d_embed
						UpperCamelCase__       =							d_proj

						UpperCamelCase__       =							cutoffs + [vocab_size]
						UpperCamelCase__       =							[0] + self.cutoffs
						UpperCamelCase__       =							div_val

						UpperCamelCase__       =							self.cutoffs[0]
						UpperCamelCase__       =							len(self.cutoffs  ) - 1
						UpperCamelCase__       =							self.shortlist_size + self.n_clusters
						UpperCamelCase__       =							keep_order

						UpperCamelCase__       =							[]
						UpperCamelCase__       =							[]




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						if self.n_clusters > 0:
									UpperCamelCase__       =							self.add_weight(
									    shape=(self.n_clusters, self.d_embed)   ,    initializer="zeros"   ,    trainable=snake_case   ,    name="cluster_weight"  )
									UpperCamelCase__       =							self.add_weight(
									    shape=(self.n_clusters,)   ,    initializer="zeros"   ,    trainable=snake_case   ,    name="cluster_bias"  )

						if self.div_val == 1:
									for i in range(len(self.cutoffs  )  ):
												if self.d_proj != self.d_embed:
															UpperCamelCase__       =							self.add_weight(
															    shape=(self.d_embed, self.d_proj)   ,    initializer="zeros"   ,    trainable=snake_case   ,    name=F'''out_projs_._{i}'''   ,    )
															self.out_projs.append(snake_case  )
												else:
															self.out_projs.append(snake_case  )
												UpperCamelCase__       =							self.add_weight(
												    shape=(self.vocab_size, self.d_embed)   ,    initializer="zeros"   ,    trainable=snake_case   ,    name=F'''out_layers_._{i}_._weight'''   ,    )
												UpperCamelCase__       =							self.add_weight(
												    shape=(self.vocab_size,)   ,    initializer="zeros"   ,    trainable=snake_case   ,    name=F'''out_layers_._{i}_._bias'''   ,    )
												self.out_layers.append((weight, bias)  )
						else:
									for i in range(len(self.cutoffs  )  ):
												UpperCamelCase__,   UpperCamelCase__       =							self.cutoff_ends[i], self.cutoff_ends[i + 1]
												UpperCamelCase__       =							self.d_embed // (self.div_val**i)

												UpperCamelCase__       =							self.add_weight(
												    shape=(d_emb_i, self.d_proj)   ,    initializer="zeros"   ,    trainable=snake_case   ,    name=F'''out_projs_._{i}'''  )
												self.out_projs.append(snake_case  )
												UpperCamelCase__       =							self.add_weight(
												    shape=(r_idx - l_idx, d_emb_i)   ,    initializer="zeros"   ,    trainable=snake_case   ,    name=F'''out_layers_._{i}_._weight'''   ,    )
												UpperCamelCase__       =							self.add_weight(
												    shape=(r_idx - l_idx,)   ,    initializer="zeros"   ,    trainable=snake_case   ,    name=F'''out_layers_._{i}_._bias'''   ,    )
												self.out_layers.append((weight, bias)  )
						super().build(snake_case  )




			@staticmethod
			def 					snake_case__					(				snake_case   ,    snake_case   ,    snake_case   ,    snake_case=None  ):

						'''simple docstring'''


						UpperCamelCase__       =							x
						if proj is not None:
									UpperCamelCase__       =							tf.einsum("ibd,ed->ibe"   ,    snake_case   ,    snake_case  )
						return tf.einsum("ibd,nd->ibn"   ,    snake_case   ,    snake_case  ) + b




			@staticmethod
			def 					snake_case__					(				snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							shape_list(snake_case  )
						UpperCamelCase__       =							tf.range(lp_size[0]   ,    dtype=target.dtype  )
						UpperCamelCase__       =							tf.stack([r, target]   ,    1  )
						return tf.gather_nd(snake_case   ,    snake_case  )







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case=True   ,    snake_case=False  ):

						'''simple docstring'''


						UpperCamelCase__       =							0
						if self.n_clusters == 0:
									UpperCamelCase__       =							self._logit(snake_case   ,    self.out_layers[0][0]   ,    self.out_layers[0][1]   ,    self.out_projs[0]  )
									if target is not None:
												UpperCamelCase__       =							tf.nn.sparse_softmax_cross_entropy_with_logits(labels=snake_case   ,    logits=snake_case  )
									UpperCamelCase__       =							tf.nn.log_softmax(snake_case   ,    axis=-1  )
						else:
									UpperCamelCase__       =							shape_list(snake_case  )
									UpperCamelCase__       =							[]
									UpperCamelCase__       =							tf.zeros(hidden_sizes[:2]  )
									for i in range(len(self.cutoffs  )  ):
												UpperCamelCase__,   UpperCamelCase__       =							self.cutoff_ends[i], self.cutoff_ends[i + 1]
												if target is not None:
															UpperCamelCase__       =							(target >= l_idx) & (target < r_idx)
															UpperCamelCase__       =							tf.where(snake_case  )
															UpperCamelCase__       =							tf.boolean_mask(snake_case   ,    snake_case  ) - l_idx

												if self.div_val == 1:
															UpperCamelCase__       =							self.out_layers[0][0][l_idx:r_idx]
															UpperCamelCase__       =							self.out_layers[0][1][l_idx:r_idx]
												else:
															UpperCamelCase__       =							self.out_layers[i][0]
															UpperCamelCase__       =							self.out_layers[i][1]

												if i == 0:
															UpperCamelCase__       =							tf.concat([cur_W, self.cluster_weight]   ,    0  )
															UpperCamelCase__       =							tf.concat([cur_b, self.cluster_bias]   ,    0  )

															UpperCamelCase__       =							self._logit(snake_case   ,    snake_case   ,    snake_case   ,    self.out_projs[0]  )
															UpperCamelCase__       =							tf.nn.log_softmax(snake_case  )
															out.append(head_logprob[..., : self.cutoffs[0]]  )
															if target is not None:
																		UpperCamelCase__       =							tf.boolean_mask(snake_case   ,    snake_case  )
																		UpperCamelCase__       =							self._gather_logprob(snake_case   ,    snake_case  )
												else:
															UpperCamelCase__       =							self._logit(snake_case   ,    snake_case   ,    snake_case   ,    self.out_projs[i]  )
															UpperCamelCase__       =							tf.nn.log_softmax(snake_case  )
															UpperCamelCase__       =							self.cutoffs[0] + i - 1  # No probability for the head cluster
															UpperCamelCase__       =							head_logprob[..., cluster_prob_idx, None] + tail_logprob
															out.append(snake_case  )
															if target is not None:
																		UpperCamelCase__       =							tf.boolean_mask(snake_case   ,    snake_case  )
																		UpperCamelCase__       =							tf.boolean_mask(snake_case   ,    snake_case  )
																		UpperCamelCase__       =							self._gather_logprob(snake_case   ,    snake_case  )
																		cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
												if target is not None:
															loss += tf.scatter_nd(snake_case   ,    -cur_logprob   ,    shape_list(snake_case  )  )
									UpperCamelCase__       =							tf.concat(snake_case   ,    axis=-1  )

						if target is not None:
									if return_mean:
												UpperCamelCase__       =							tf.reduce_mean(snake_case  )
									# Add the training-time loss value to the layer using `self.add_loss()`.
									self.add_loss(snake_case  )

									# Log the loss as a metric (we could log arbitrary metrics,
									# including different metrics for training and inference.
									self.add_metric(snake_case   ,    name=self.name   ,    aggregation="mean" if return_mean else ""  )

						return out






from __future__ import annotations

import numpy as np
def 							UpperCamelCase_(					_A :list[float]	)->		Optional[Any]:
			return np.maximum(0					,  _A	)


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


